Search Results (3,736)

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural system installed in a plant growth chamber for hydroponic cultivation under controlled conditions. The growth chamber is equipped with sensors for air temperature, relative humidity (RH), carbon dioxide (CO₂) and photosynthetically active photon flux, as well as control mechanisms such as humidifiers, full-spectrum Light Emitting Diode (LED) lamps, mini split air conditioner, pumps, a Wi-Fi surveillance camera, remote monitoring via a web application and three Nutrient Film Technique (NFT) hydroponic systems with a capacity of ten plants each. An ATmega2560 microcontroller manages the smart system using the MODBUS RS-485 communication protocol. To validate the proper functionality of the proposed system, a case study was conducted using lettuce crops, in which the impact of different nutrient solution concentrations (50%, 75% and 100%) on the phenotypic development and nutritional content of the plants was evaluated. The results obtained from the cultivation experiment, analyzed through analysis of variance (ANOVA), show that the treatment with 75% nutrient concentration provides an appropriate balance between resource use and nutritional quality, without affecting the chlorophyll content. This system represents a scalable and replicable alternative for protected agriculture. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Recently, the usage of mobile phone-based games has increased due to the growing accessibility and convenience they provide. Using a descriptive-quantitative design, a disaster-based mobile application was developed in this study to enhance disaster literacy among the private senior high schools in science, technology, engineering, and mathematics (STEM) education in Davao City, the Philippines. The developed application was provided together with survey questionnaires to 364 students randomly selected from different schools in Davao City usingF a simple random sampling method. The technology acceptance (TAM) model was used to explain how users accepted the new technology. The mobile application was designed with features in four disaster scenarios—fire, flood, volcano, and earthquake. The results revealed a high acceptance, with an average score of the perceived usefulness (PE) of 4.52, perceived ease of use (PEOU) of 4.44, and a behavioral intention (BI) of 4.12. The students accepted the application to enhance disaster risk reduction and management. Aligned with SDG 4 and SDG 11, the application can be used to equip users with the knowledge to respond to disasters and ensure community resilience. Full article

Nitrogen (N) fertilizer management in winter wheat production faces challenges from volatilization losses and sub-optimal application strategies. This is particularly problematic in the Southern Great Plains, where environmental conditions during top-dressing periods favor N losses. This study evaluated the effects of a fertilizer placement method, enhanced-efficiency fertilizers, and application timing on grain yield and protein concentration (GPC) across six site-years in Oklahoma (2016–2018). Treatments included broadcast applications of untreated urea and SuperU^® (urease/nitrification inhibitor-treated urea). These were compared with subsurface placement using single-disc and double-disc drilling systems, applied at 67 kg N ha⁻¹ during January, February, or March. Subsurface placement increased the grain yield by 324–391 kg ha⁻¹ compared to broadcast applications at sites with favorable soil conditions. However, responses varied significantly across environments. Enhanced-efficiency fertilizers showed limited advantages over untreated urea. Benefits were most pronounced during February applications under conditions favoring volatilization losses. Application timing effects were more consistent for GPC than for the yield. Later applications (February–March) increased GPC by 0.8–1.2% compared to January applications. Treatment efficacy was strongly influenced by soil pH, equipment performance, and post-application environmental conditions. This indicates that N management benefits are highly site-specific. These findings demonstrate that subsurface placement can improve nitrogen use efficiency (NUE) under appropriate conditions. However, success depends on matching application strategies to local soil and environmental factors rather than adopting universal recommendations. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

The rapid development of the modern power system puts forward high requirements for economic dispatch, and the defects of the traditional centralized economic dispatch method with low security and poor optimization effect have been difficult to adapt to the development of power system. Therefore, finding an economic dispatch method that reduces electricity generation costs and CO₂ emissions is important. This study establishes a multi-region distributed optimization model and combines the multi-region distributed optimization model with a multi-gradient optimization algorithm to propose a multi-region distributed multi-gradient whale optimization algorithm (MRDMGWOA). In this study, MRDMGWOA is simulated on the IEEE 39 system and 118 system, and its performance is compared with other heuristic algorithms. The results show that: (1) in the IEEE 39 system, MRDMGWOA reduces the power generation cost and CO₂ emission by 17% and 22%, respectively, and reduces the computation time by 16.14 s compared with the centralized optimization; (2) in the IEEE 118 system, the two metrics are further optimized, with a 20% and 17% reduction in the cost and emission, respectively, and an improvement in the computational efficiency by 45.46 s; (3) in the spacing, hypervolume, and Euclidian metrics evaluation, MRDMGWOA outperforms other algorithms; (4) compared with the existing DMOGWO and DMOMFO, the computation time of MRDMGWOA is reduced by 177.49 s and 124.15 s, respectively, and the scheduling scheme obtained by MRDMGWOA is more optimal than DMOGWO and DMOMFO. Full article

Under normal conditions, the novel zinc blende beryllium oxide (zb BeO) exhibits in a metastable crystalline phase, which is less stable than its wurtzite counterpart. Ultrathin zb BeO epifilms have recently gained significant interest to create a wide range of advanced high-resolution, high-frequency, flexible, transparent, nano-electronic and nanophotonic modules. BeO-based ultraviolet photodetectors and biosensors are playing important roles in providing safety and efficiency to nuclear reactors for their optimum operations. In thermal management, BeO epifilms have also been used for many high-tech devices including medical equipment. Phonon characteristics of zb BeO at ambient and high-pressure P ≠ 0 GPa are required in the development of electronics that demand enhanced heat dissipation for improving heat sink performance to lower the operating temperature. Here, we have reported methodical simulations to comprehend P-dependent structural, phonon and thermodynamical properties by using a realistic rigid-ion model (RIM). Unlike zb ZnO, the study of the Grüneisen parameter γ(T) and thermal expansion coefficient α(T) in zb BeO has revealed atypical behavior. Possible reasons for such peculiar trends are attributed to the combined effect of the short bond length and strong localization of electron charge close to the small core size Be atom in BeO. Results of RIM calculations are compared/contrasted against the limited experimental and first-principle data. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

Background: Brain health is a growing public health priority due to the high global burden of neurological and mental disorders. Promoting brain health across the lifespan supports individual and societal well-being, creativity, and productivity. Objective: To address the need for specialized education in this field, the University of Bern developed a Certificate of Advanced Studies (CAS) in Brain Health. This article outlines the program’s rationale, structure, and goals. Program Description: The one-year, 15 ECTS-credit program is primarily online and consists of four modules: (1) Introduction to Brain Health, (2) Brain Disorders, (3) Risk Factors, Protective Factors and Interventions, and (4) Brain Health Implementation. It offers a multidisciplinary, interprofessional, life-course approach, integrating theory with practice through case studies and interactive sessions. Designed for healthcare and allied professionals, the CAS equips participants with skills to promote brain health in clinical, research, and public health contexts. Given the shortage of trained professionals in Europe and globally, the program seeks to build a new generation of brain health advocates. It aims to inspire action and initiatives that support the prevention, early detection, and management of brain disorders. Conclusions: The CAS in Brain Health is an innovative educational response to a pressing global need. By fostering interdisciplinary expertise and practical skills, it enhances professional development and supports improved brain health outcomes at individual and population levels. Full article

Biofilms are structured microbial communities that pose significant challenges to food safety and quality within the food-processing industry. Their formation on equipment and surfaces enables persistent contamination, microbial resistance, and recurring outbreaks of foodborne illness. This review provides a comprehensive synthesis of current knowledge on biofilm formation mechanisms, genetic regulation, and the unique behavior of multi-species biofilms. The review evaluates modern detection and monitoring technologies, including PCR, biosensors, and advanced microscopy, and compares their effectiveness in industrial contexts. Real-world outbreak data and a global economic impact analysis underscore the urgency for more effective regulatory frameworks and sanitation innovations. The findings highlight the critical need for integrated, proactive biofilm management approaches to safeguard food safety, reduce public health risks, and minimize economic losses across global food sectors. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article

With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in general, full-body motion estimation requires motion capture systems or multiple inertial sensors, making it necessary to directly measure movement itself. In this study, we propose estimating full-body posture using inverse kinematics based on trunk posture and limb-end information collected through wearable devices. To enhance estimation accuracy in this underdetermined problem, we employ Physics-Informed Neural Networks (PINNs), which efficiently learn using physical laws as a loss function, along with a high-precision inverse kinematics model of a digital human. Through this approach, we enable high-accuracy full-body posture estimation even with wearable devices in underdetermined scenarios. Full article

Technological roadmaps are essential tools for managing and planning complex projects, especially in the rapidly evolving field of space exploration. Defined as dynamic schedules, they support strategic and long-term planning while coordinating current and future objectives with particular technology solutions. Currently, the available methodologies are mostly built on experts’ opinions and in just few cases, methodologies and tools have been developed to support the decision makers with a rational approach. In any case, all the available approaches are meant to draw “ideal” maturation plans. Therefore, it is deemed essential to develop an integrate new algorithms able to decision guidelines on “non-nominal” scenarios. In this context, Politecnico di Torino, in collaboration with the European Space Agency (ESA) and Thales Alenia Space–Italia, developed the Technology Roadmapping Strategy (TRIS), a multi-step process designed to create robust and data-driven roadmaps. However, one of the main concerns with its initial implementation was that TRIS did not account for time and budget estimates specific to the space exploration environment, nor was it capable of generating alternative development paths under constrained conditions. This paper discloses two main significant updates to TRIS methodology: (1) improved time and budget estimation to better reflect the specific challenges of space exploration scenarios and (2) the capability of generating alternative roadmaps, i.e., alternative technological maturation paths in resource-constrained scenarios, balancing financial and temporal limitations. The application of the developed routines to available case studies confirms the tool’s ability to provide consistent planning outputs across multiple scenarios without exceeding 20% deviation from expert-based judgements available as reference. The results demonstrate the potential of the enhanced methodology in supporting strategic decision making in early-phase mission planning, ensuring adaptability to changing conditions, optimized use of time and financial resources, as well as guaranteeing an improved flexibility of the tool. By integrating data-driven prioritization, uncertainty modeling, and resource-constrained planning, TRIS equips mission planners with reliable tools to navigate the complexities of space exploration projects. This methodology ensures that roadmaps remain adaptable to changing conditions and optimized for real-world challenges, supporting the sustainable advancement of space exploration initiatives. Full article

This study explores the internationalization of small and medium enterprises (SMEs), emphasizing the critical role of competent managerial abilities. Specifically, it investigates the sustainable role of managerial capabilities in directly facilitating SMEs’ entry into international markets, or whether these capabilities first assist in risk management and resource utilization, supporting international expansion. We propose that SMEs with skilled and capable managers are better equipped to manage internal risks and leverage available resources, thereby enhancing their internationalization efforts. Drawing on empirical data from 191 Chinese SMEs, our findings support the proposed model, revealing that managerial capabilities contribute to internationalization indirectly—this relationship is fully mediated by risk management and resource utilization. This study recommends that SMEs prioritize building a sustainable management team capable of navigating internal challenges to successfully pursue international growth. Our research contributes to the resource-based view and the Uppsala model of internationalization by contextualizing the role of managerial capabilities, risk management, and resource utilization in the internationalization processes of SMEs. Full article